Overview

Dataset statistics

Number of variables16
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows33
Duplicate rows (%)3.3%
Total size in memory125.1 KiB
Average record size in memory128.1 B

Variable types

Numeric9
Categorical5
Text2

Alerts

Dataset has 33 (3.3%) duplicate rowsDuplicates
brand is highly overall correlated with max_power and 2 other fieldsHigh correlation
engine is highly overall correlated with max_power and 3 other fieldsHigh correlation
fuel is highly overall correlated with max_torque_rpmHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with brand and 4 other fieldsHigh correlation
max_torque_rpm is highly overall correlated with fuel and 1 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with brand and 5 other fieldsHigh correlation
torque_val is highly overall correlated with engine and 3 other fieldsHigh correlation
transmission is highly overall correlated with brand and 2 other fieldsHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (52.7%)Imbalance

Reproduction

Analysis started2025-12-09 12:36:46.229501
Analysis finished2025-12-09 12:37:12.128612
Duration25.9 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.681
Minimum1995
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:12.330838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0121486
Coefficient of variation (CV)0.001992445
Kurtosis1.2158841
Mean2013.681
Median Absolute Deviation (MAD)3
Skewness-1.0223557
Sum2013681
Variance16.097336
MonotonicityNot monotonic
2025-12-09T12:37:12.457382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2017134
13.4%
2016106
10.6%
201596
9.6%
201891
9.1%
201185
8.5%
201283
8.3%
201479
7.9%
201376
7.6%
201964
6.4%
201049
 
4.9%
Other values (14)137
13.7%
ValueCountFrequency (%)
19951
 
0.1%
19981
 
0.1%
19995
 
0.5%
20001
 
0.1%
20012
 
0.2%
20024
 
0.4%
20038
 
0.8%
200410
1.0%
200510
1.0%
200620
2.0%
ValueCountFrequency (%)
20204
 
0.4%
201964
6.4%
201891
9.1%
2017134
13.4%
2016106
10.6%
201596
9.6%
201479
7.9%
201376
7.6%
201283
8.3%
201185
8.5%

selling_price
Real number (ℝ)

High correlation 

Distinct274
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617901.04
Minimum31000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:12.610239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31000
5-th percentile100000
Q1250000
median434999
Q3670000
95-th percentile1904049
Maximum6000000
Range5969000
Interquartile range (IQR)420000

Descriptive statistics

Standard deviation758553.86
Coefficient of variation (CV)1.22763
Kurtosis21.438457
Mean617901.04
Median Absolute Deviation (MAD)205000
Skewness4.2148309
Sum6.1790104 × 108
Variance5.7540396 × 1011
MonotonicityNot monotonic
2025-12-09T12:37:12.823720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000029
 
2.9%
35000028
 
2.8%
60000028
 
2.8%
55000025
 
2.5%
40000024
 
2.4%
65000024
 
2.4%
25000022
 
2.2%
75000022
 
2.2%
50000022
 
2.2%
45000016
 
1.6%
Other values (264)760
76.0%
ValueCountFrequency (%)
310001
 
0.1%
339831
 
0.1%
350001
 
0.1%
400001
 
0.1%
450005
0.5%
460001
 
0.1%
500002
 
0.2%
520002
 
0.2%
550003
0.3%
555991
 
0.1%
ValueCountFrequency (%)
60000002
 
0.2%
55000005
0.5%
54000002
 
0.2%
51500003
 
0.3%
41000001
 
0.1%
38000002
 
0.2%
37500001
 
0.1%
34000001
 
0.1%
32510001
 
0.1%
32000008
0.8%

km_driven
Real number (ℝ)

High correlation 

Distinct260
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71393.341
Minimum1303
Maximum375000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:13.068399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1303
5-th percentile9190
Q137000
median61500
Q3100000
95-th percentile160000
Maximum375000
Range373697
Interquartile range (IQR)63000

Descriptive statistics

Standard deviation48486.219
Coefficient of variation (CV)0.67914203
Kurtosis3.8337561
Mean71393.341
Median Absolute Deviation (MAD)28500
Skewness1.4228571
Sum71393341
Variance2.3509134 × 109
MonotonicityNot monotonic
2025-12-09T12:37:13.306519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000066
 
6.6%
7000058
 
5.8%
6000055
 
5.5%
8000054
 
5.4%
4000046
 
4.6%
5000044
 
4.4%
9000038
 
3.8%
11000035
 
3.5%
10000033
 
3.3%
3000027
 
2.7%
Other values (250)544
54.4%
ValueCountFrequency (%)
13031
 
0.1%
20007
0.7%
23881
 
0.1%
26001
 
0.1%
31001
 
0.1%
35002
 
0.2%
35641
 
0.1%
40001
 
0.1%
43371
 
0.1%
50009
0.9%
ValueCountFrequency (%)
3750001
0.1%
3000002
0.2%
2980001
0.1%
2910001
0.1%
2700001
0.1%
2650001
0.1%
2640001
0.1%
2600001
0.1%
2500001
0.1%
2480001
0.1%

fuel
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Diesel
534 
Petrol
457 
CNG
 
5
LPG
 
4

Length

Max length6
Median length6
Mean length5.973
Min length3

Characters and Unicode

Total characters5973
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel534
53.4%
Petrol457
45.7%
CNG5
 
0.5%
LPG4
 
0.4%

Length

2025-12-09T12:37:13.486548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T12:37:13.579035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel534
53.4%
petrol457
45.7%
cng5
 
0.5%
lpg4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1525
25.5%
l991
16.6%
D534
 
8.9%
i534
 
8.9%
s534
 
8.9%
P461
 
7.7%
t457
 
7.7%
r457
 
7.7%
o457
 
7.7%
G9
 
0.2%
Other values (3)14
 
0.2%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Individual
837 
Dealer
135 
Trustmark Dealer
 
28

Length

Max length16
Median length10
Mean length9.628
Min length6

Characters and Unicode

Total characters9628
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual837
83.7%
Dealer135
 
13.5%
Trustmark Dealer28
 
2.8%

Length

2025-12-09T12:37:13.682421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T12:37:13.813225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual837
81.4%
dealer163
 
15.9%
trustmark28
 
2.7%

Most occurring characters

ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
n837
8.7%
v837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
n837
8.7%
v837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
n837
8.7%
v837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d1674
17.4%
i1674
17.4%
a1028
10.7%
l1000
10.4%
u865
9.0%
I837
8.7%
n837
8.7%
v837
8.7%
e326
 
3.4%
r219
 
2.3%
Other values (7)331
 
3.4%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Manual
877 
Automatic
123 

Length

Max length9
Median length6
Mean length6.369
Min length6

Characters and Unicode

Total characters6369
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowAutomatic
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual877
87.7%
Automatic123
 
12.3%

Length

2025-12-09T12:37:13.992535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T12:37:14.091823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual877
87.7%
automatic123
 
12.3%

Most occurring characters

ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1877
29.5%
u1000
15.7%
M877
13.8%
n877
13.8%
l877
13.8%
t246
 
3.9%
A123
 
1.9%
o123
 
1.9%
m123
 
1.9%
i123
 
1.9%

owner
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
First Owner
623 
Second Owner
278 
Third Owner
71 
Fourth & Above Owner
 
27
Test Drive Car
 
1

Length

Max length20
Median length11
Mean length11.524
Min length11

Characters and Unicode

Total characters11524
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFirst Owner
2nd rowFirst Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowSecond Owner

Common Values

ValueCountFrequency (%)
First Owner623
62.3%
Second Owner278
27.8%
Third Owner71
 
7.1%
Fourth & Above Owner27
 
2.7%
Test Drive Car1
 
0.1%

Length

2025-12-09T12:37:14.189420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T12:37:14.282707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
owner999
48.6%
first623
30.3%
second278
 
13.5%
third71
 
3.5%
fourth27
 
1.3%
27
 
1.3%
above27
 
1.3%
test1
 
< 0.1%
drive1
 
< 0.1%
car1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1722
14.9%
e1306
11.3%
n1277
11.1%
1055
9.2%
O999
8.7%
w999
8.7%
i695
6.0%
t651
 
5.6%
F650
 
5.6%
s624
 
5.4%
Other values (14)1546
13.4%

mileage
Real number (ℝ)

Distinct237
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.295872
Minimum0
Maximum28.4
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:14.502312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.8
Q116.5
median19.3
Q322.3
95-th percentile25.5
Maximum28.4
Range28.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.9498231
Coefficient of variation (CV)0.20469783
Kurtosis-0.060685067
Mean19.295872
Median Absolute Deviation (MAD)2.8
Skewness-0.1405952
Sum19295.872
Variance15.601103
MonotonicityNot monotonic
2025-12-09T12:37:14.862963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.326
 
2.6%
18.623
 
2.3%
18.922
 
2.2%
21.121
 
2.1%
19.721
 
2.1%
16.117
 
1.7%
12.816
 
1.6%
1716
 
1.6%
22.7415
 
1.5%
18.215
 
1.5%
Other values (227)808
80.8%
ValueCountFrequency (%)
01
 
0.1%
9.51
 
0.1%
9.9629629631
 
0.1%
10.53
0.3%
10.561
 
0.1%
10.751
 
0.1%
10.912
0.2%
10.931
 
0.1%
111
 
0.1%
11.181
 
0.1%
ValueCountFrequency (%)
28.411
1.1%
28.092
 
0.2%
27.395
0.5%
27.33
 
0.3%
27.282
 
0.2%
26.595
0.5%
26.212
 
0.2%
2610
1.0%
25.834
 
0.4%
25.8081
 
0.1%

engine
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1454.876
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:15.091061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2523
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation521.99574
Coefficient of variation (CV)0.35879054
Kurtosis0.90097598
Mean1454.876
Median Absolute Deviation (MAD)248
Skewness1.1890629
Sum1454876
Variance272479.55
MonotonicityNot monotonic
2025-12-09T12:37:15.422136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248135
 
13.5%
1197105
 
10.5%
79663
 
6.3%
99857
 
5.7%
139651
 
5.1%
217949
 
4.9%
149847
 
4.7%
249432
 
3.2%
119931
 
3.1%
149723
 
2.3%
Other values (78)407
40.7%
ValueCountFrequency (%)
6247
 
0.7%
79663
6.3%
79911
 
1.1%
81418
 
1.8%
9091
 
0.1%
9365
 
0.5%
9932
 
0.2%
9952
 
0.2%
99857
5.7%
9997
 
0.7%
ValueCountFrequency (%)
36041
 
0.1%
31982
 
0.2%
29933
0.3%
29872
 
0.2%
29825
0.5%
29566
0.6%
29531
 
0.1%
28351
 
0.1%
27555
0.5%
26961
 
0.1%

max_power
Real number (ℝ)

High correlation 

Distinct180
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.84167
Minimum34.2
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:15.681372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.2
5-th percentile47.3
Q169
median82.425
Q3102
95-th percentile163.94
Maximum280
Range245.8
Interquartile range (IQR)33

Descriptive statistics

Standard deviation34.893389
Coefficient of variation (CV)0.38411214
Kurtosis3.7253025
Mean90.84167
Median Absolute Deviation (MAD)15.385
Skewness1.5941364
Sum90841.67
Variance1217.5486
MonotonicityNot monotonic
2025-12-09T12:37:15.997937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7443
 
4.3%
81.8634
 
3.4%
88.529
 
2.9%
47.324
 
2.4%
81.824
 
2.4%
67.122
 
2.2%
46.321
 
2.1%
88.7320
 
2.0%
88.720
 
2.0%
67.0419
 
1.9%
Other values (170)744
74.4%
ValueCountFrequency (%)
34.22
 
0.2%
355
 
0.5%
3715
1.5%
37.482
 
0.2%
381
 
0.1%
451
 
0.1%
46.321
2.1%
47.324
2.4%
521
 
0.1%
52.84
 
0.4%
ValueCountFrequency (%)
2801
 
0.1%
270.91
 
0.1%
254.792
 
0.2%
2411
 
0.1%
2352
 
0.2%
214.563
 
0.3%
2041
 
0.1%
1972
 
0.2%
1909
0.9%
187.741
 
0.1%

seats
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.403
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:16.364618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum9
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91292082
Coefficient of variation (CV)0.16896554
Kurtosis1.890972
Mean5.403
Median Absolute Deviation (MAD)0
Skewness1.6728153
Sum5403
Variance0.83342442
MonotonicityNot monotonic
2025-12-09T12:37:16.510065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5777
77.7%
7161
 
16.1%
424
 
2.4%
823
 
2.3%
68
 
0.8%
97
 
0.7%
ValueCountFrequency (%)
424
 
2.4%
5777
77.7%
68
 
0.8%
7161
 
16.1%
823
 
2.3%
97
 
0.7%
ValueCountFrequency (%)
97
 
0.7%
823
 
2.3%
7161
 
16.1%
68
 
0.8%
5777
77.7%
424
 
2.4%

torque_val
Real number (ℝ)

High correlation 

Distinct145
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.61744
Minimum48
Maximum1421.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:16.714838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile69
Q1111.7
median160
Q3205
95-th percentile350
Maximum1421.96
Range1373.96
Interquartile range (IQR)93.3

Descriptive statistics

Standard deviation103.73442
Coefficient of variation (CV)0.58403284
Kurtosis33.266603
Mean177.61744
Median Absolute Deviation (MAD)48.3
Skewness3.67296
Sum177617.44
Variance10760.829
MonotonicityNot monotonic
2025-12-09T12:37:17.085905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20089
 
8.9%
19067
 
6.7%
9052
 
5.2%
16034
 
3.4%
11330
 
3.0%
11429
 
2.9%
6924
 
2.4%
6221
 
2.1%
11020
 
2.0%
74.518
 
1.8%
Other values (135)616
61.6%
ValueCountFrequency (%)
484
 
0.4%
513
 
0.3%
5917
1.7%
59.821
 
0.1%
6221
2.1%
6924
2.4%
7211
1.1%
74.518
1.8%
76.491
 
0.1%
771
 
0.1%
ValueCountFrequency (%)
1421.961
 
0.1%
1274.861
 
0.1%
6202
 
0.2%
6001
 
0.1%
5401
 
0.1%
519.751
 
0.1%
4801
 
0.1%
4702
 
0.2%
4503
0.3%
4307
0.7%

max_torque_rpm
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2888.4466
Minimum1470
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:17.416984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1470
5-th percentile1750
Q12125
median2400
Q34000
95-th percentile4500
Maximum5000
Range3530
Interquartile range (IQR)1875

Descriptive statistics

Standard deviation953.13648
Coefficient of variation (CV)0.32998238
Kurtosis-1.2098848
Mean2888.4466
Median Absolute Deviation (MAD)600
Skewness0.49657061
Sum2888446.6
Variance908469.15
MonotonicityNot monotonic
2025-12-09T12:37:17.597204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4000120
12.0%
2000111
 
11.1%
3500102
 
10.2%
175070
 
7.0%
212566
 
6.6%
240060
 
6.0%
250057
 
5.7%
300043
 
4.3%
225038
 
3.8%
450037
 
3.7%
Other values (39)296
29.6%
ValueCountFrequency (%)
14709
 
0.9%
15004
 
0.4%
175070
7.0%
180021
 
2.1%
190011
 
1.1%
19504
 
0.4%
2000111
11.1%
20501
 
0.1%
21005
 
0.5%
212566
6.6%
ValueCountFrequency (%)
50002
 
0.2%
48503
 
0.3%
480017
1.7%
47501
 
0.1%
47003
 
0.3%
46009
 
0.9%
450037
3.7%
440013
 
1.3%
438611
 
1.1%
43004
 
0.4%

brand
Categorical

High correlation 

Distinct25
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Maruti
290 
Hyundai
198 
Tata
106 
Mahindra
90 
Toyota
59 
Other values (20)
257 

Length

Max length10
Median length9
Mean length6.125
Min length3

Characters and Unicode

Total characters6125
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowMahindra
2nd rowTata
3rd rowHonda
4th rowHonda
5th rowTata

Common Values

ValueCountFrequency (%)
Maruti290
29.0%
Hyundai198
19.8%
Tata106
 
10.6%
Mahindra90
 
9.0%
Toyota59
 
5.9%
Honda57
 
5.7%
Ford50
 
5.0%
Renault29
 
2.9%
Chevrolet24
 
2.4%
Volkswagen15
 
1.5%
Other values (15)82
 
8.2%

Length

2025-12-09T12:37:17.792392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti290
29.0%
hyundai198
19.8%
tata106
 
10.6%
mahindra90
 
9.0%
toyota59
 
5.9%
honda57
 
5.7%
ford50
 
5.0%
renault29
 
2.9%
chevrolet24
 
2.4%
volkswagen15
 
1.5%
Other values (15)82
 
8.2%

Most occurring characters

ValueCountFrequency (%)
a1092
17.8%
i606
9.9%
u541
8.8%
t522
8.5%
r468
7.6%
d416
 
6.8%
n407
 
6.6%
M399
 
6.5%
o299
 
4.9%
y257
 
4.2%
Other values (28)1118
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)6125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1092
17.8%
i606
9.9%
u541
8.8%
t522
8.5%
r468
7.6%
d416
 
6.8%
n407
 
6.6%
M399
 
6.5%
o299
 
4.9%
y257
 
4.2%
Other values (28)1118
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1092
17.8%
i606
9.9%
u541
8.8%
t522
8.5%
r468
7.6%
d416
 
6.8%
n407
 
6.6%
M399
 
6.5%
o299
 
4.9%
y257
 
4.2%
Other values (28)1118
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1092
17.8%
i606
9.9%
u541
8.8%
t522
8.5%
r468
7.6%
d416
 
6.8%
n407
 
6.6%
M399
 
6.5%
o299
 
4.9%
y257
 
4.2%
Other values (28)1118
18.3%

model
Text

Distinct130
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:18.166671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7.5
Mean length4.838
Min length1

Characters and Unicode

Total characters4838
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)3.0%

Sample

1st rowXylo
2nd rowNexon
3rd rowCivic
4th rowCity
5th rowIndica
ValueCountFrequency (%)
swift83
 
8.3%
alto59
 
5.9%
i2055
 
5.5%
innova31
 
3.1%
wagon30
 
3.0%
indica26
 
2.6%
bolero25
 
2.5%
verna24
 
2.4%
city24
 
2.4%
i1023
 
2.3%
Other values (119)620
62.0%
2025-12-09T12:37:18.636290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o462
 
9.5%
i413
 
8.5%
a384
 
7.9%
t353
 
7.3%
n311
 
6.4%
r290
 
6.0%
e247
 
5.1%
S182
 
3.8%
l147
 
3.0%
0146
 
3.0%
Other values (48)1903
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o462
 
9.5%
i413
 
8.5%
a384
 
7.9%
t353
 
7.3%
n311
 
6.4%
r290
 
6.0%
e247
 
5.1%
S182
 
3.8%
l147
 
3.0%
0146
 
3.0%
Other values (48)1903
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o462
 
9.5%
i413
 
8.5%
a384
 
7.9%
t353
 
7.3%
n311
 
6.4%
r290
 
6.0%
e247
 
5.1%
S182
 
3.8%
l147
 
3.0%
0146
 
3.0%
Other values (48)1903
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o462
 
9.5%
i413
 
8.5%
a384
 
7.9%
t353
 
7.3%
n311
 
6.4%
r290
 
6.0%
e247
 
5.1%
S182
 
3.8%
l147
 
3.0%
0146
 
3.0%
Other values (48)1903
39.3%

conf
Text

Distinct567
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2025-12-09T12:37:19.021350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length25
Mean length11.093
Min length1

Characters and Unicode

Total characters11093
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique373 ?
Unique (%)37.3%

Sample

1st rowE4 BS IV
2nd row1 5 Revotorq XE
3rd row1 8 S AT
4th rowi DTEC VX
5th rowVista Aura 1 2 Safire BSIV
ValueCountFrequency (%)
1245
 
8.2%
2150
 
5.0%
bsiv79
 
2.7%
576
 
2.6%
vxi74
 
2.5%
plus64
 
2.1%
crdi61
 
2.0%
lxi57
 
1.9%
vdi51
 
1.7%
sportz42
 
1.4%
Other values (329)2081
69.8%
2025-12-09T12:37:19.677411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1980
 
17.8%
i529
 
4.8%
S506
 
4.6%
I500
 
4.5%
V395
 
3.6%
1393
 
3.5%
X389
 
3.5%
a376
 
3.4%
D376
 
3.4%
t362
 
3.3%
Other values (52)5287
47.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)11093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1980
 
17.8%
i529
 
4.8%
S506
 
4.6%
I500
 
4.5%
V395
 
3.6%
1393
 
3.5%
X389
 
3.5%
a376
 
3.4%
D376
 
3.4%
t362
 
3.3%
Other values (52)5287
47.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1980
 
17.8%
i529
 
4.8%
S506
 
4.6%
I500
 
4.5%
V395
 
3.6%
1393
 
3.5%
X389
 
3.5%
a376
 
3.4%
D376
 
3.4%
t362
 
3.3%
Other values (52)5287
47.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1980
 
17.8%
i529
 
4.8%
S506
 
4.6%
I500
 
4.5%
V395
 
3.6%
1393
 
3.5%
X389
 
3.5%
a376
 
3.4%
D376
 
3.4%
t362
 
3.3%
Other values (52)5287
47.7%

Interactions

2025-12-09T12:37:09.843598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:48.031424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:51.857704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:54.073983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:56.154993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:57.925250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:59.865917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:02.097893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:05.108554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.067602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:48.320947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:52.244684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:54.311586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:56.350185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:58.122546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:00.042933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:02.493388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:05.462831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.283145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:48.979834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:52.467889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:54.535871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:56.549407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:58.349590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:00.235575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:02.860925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:08.513716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.514778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:49.581125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:52.709328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:54.754417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:56.752713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:58.553837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:00.480187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:03.115747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:08.720991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.721563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:49.911913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:52.900638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:54.961418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:56.938424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:58.746084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:00.682795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:03.410435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:08.843124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.848337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:50.190799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:53.180904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:55.217969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:57.135334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:58.939032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:00.898216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:03.688421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:08.981803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:10.982624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:50.526416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:53.387566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:55.456696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:57.356172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:59.135056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:01.151835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:04.013311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:09.213502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:11.254926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:51.185075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:53.586779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:55.667055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:57.538785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:59.356864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:01.366453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:04.379894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:09.417949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:11.531788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:51.450099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:53.801105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:55.890300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:57.737616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:36:59.595008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:01.705804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:04.578838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T12:37:09.623651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-09T12:37:19.871051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
brandenginefuelkm_drivenmax_powermax_torque_rpmmileageownerseatsseller_typeselling_pricetorque_valtransmissionyear
brand1.0000.4630.2320.1650.5470.3810.2810.1110.3350.3630.5470.3720.5730.153
engine0.4631.0000.4380.2610.771-0.438-0.4540.0400.5430.2490.5160.8540.4990.015
fuel0.2320.4381.0000.1740.2380.5360.2200.0000.2200.1060.1500.4520.0000.133
km_driven0.1650.2610.1741.0000.046-0.285-0.2050.1640.2360.142-0.3280.2050.243-0.597
max_power0.5470.7710.2380.0461.000-0.171-0.3380.0670.3420.2550.6670.8220.5820.212
max_torque_rpm0.381-0.4380.536-0.285-0.1711.000-0.1400.074-0.3140.280-0.232-0.6230.2190.017
mileage0.281-0.4540.220-0.205-0.338-0.1401.0000.092-0.4420.073-0.015-0.1920.2590.317
owner0.1110.0400.0000.1640.0670.0740.0921.0000.0600.1740.1650.0730.1470.281
seats0.3350.5430.2200.2360.342-0.314-0.4420.0601.0000.0250.2970.4560.0340.027
seller_type0.3630.2490.1060.1420.2550.2800.0730.1740.0251.0000.3640.1700.3620.196
selling_price0.5470.5160.150-0.3280.667-0.232-0.0150.1650.2970.3641.0000.6300.6280.710
torque_val0.3720.8540.4520.2050.822-0.623-0.1920.0730.4560.1700.6301.0000.4410.158
transmission0.5730.4990.0000.2430.5820.2190.2590.1470.0340.3620.6280.4411.0000.308
year0.1530.0150.133-0.5970.2120.0170.3170.2810.0270.1960.7100.1580.3081.000

Missing values

2025-12-09T12:37:11.800424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-09T12:37:11.989559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatstorque_valmax_torque_rpmbrandmodelconf
02010229999168000DieselIndividualManualFirst Owner14.002498112.007260.002000.00MahindraXyloE4 BS IV
1201766500025000DieselIndividualManualFirst Owner21.501497108.505260.002125.00TataNexon1 5 Revotorq XE
22007175000218463PetrolIndividualAutomaticFirst Owner12.901799130.005172.004300.00HondaCivic1 8 S AT
32015635000173000DieselIndividualManualFirst Owner25.10149898.605200.001750.00HondaCityi DTEC VX
4201113000070000PetrolIndividualManualSecond Owner16.50117265.00596.003000.00TataIndicaVista Aura 1 2 Safire BSIV
5201997500012584DieselDealerManualFirst Owner16.552498105.006247.001900.00MahindraTharCRDe
6201115000035000PetrolIndividualManualFirst Owner18.0099562.00590.304200.00ChevroletSpark1 0 LS
7201227500070000PetrolIndividualManualSecond Owner18.50119785.805114.004000.00MarutiRitzZXi
8201114000072000PetrolIndividualManualSecond Owner19.7079646.30562.003000.00MarutiAltoLX
9201685000058000DieselIndividualManualFirst Owner19.671582126.205259.902325.00HyundaiCreta1 6 CRDi SX
yearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatstorque_valmax_torque_rpmbrandmodelconf
99020079500070000PetrolIndividualManualSecond Owner19.7079646.30562.003000.00MarutiAltoLXi
991201237600026000PetrolIndividualManualFirst Owner19.40119886.805109.004500.00HondaBrioV MT
992200685000150000PetrolIndividualManualSecond Owner19.7079646.30562.003000.00MarutiAltoLXi
993199952000100000PetrolIndividualManualFirst Owner16.1079637.00459.002500.00Maruti800DX
9942010240000143000PetrolIndividualManualFirst Owner17.50129885.805114.004000.00MarutiSwiftDzire VXi
9952008250000100000PetrolIndividualManualSecond Owner19.81108668.05599.044500.00Hyundaii10Magna 1 1L
996201744000050000PetrolIndividualManualSecond Owner18.60119781.835114.704000.00Hyundaii202015 2017 Sportz 1 2
997200934000040000DieselIndividualManualFirst Owner23.00139690.005219.672250.00Hyundaii20Era
998201235000025000PetrolIndividualManualFirst Owner20.36119778.905111.804000.00Hyundaii10Asta
9992016700000110000DieselIndividualManualFirst Owner26.00149898.605200.001750.00HondaCityi DTec SV

Duplicate rows

Most frequently occurring

yearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatstorque_valmax_torque_rpmbrandmodelconf# duplicates
12201655000056494PetrolTrustmark DealerManualFirst Owner18.20119988.705110.004800.00HondaJazzVX8
162016200000068089PetrolTrustmark DealerAutomaticFirst Owner19.162494157.705213.004500.00ToyotaCamry2 5 Hybrid6
222017320000045000DieselDealerAutomaticFirst Owner19.331999177.005430.002125.00JaguarXF2 0 Portfolio6
27201824750002000DieselDealerAutomaticFirst Owner16.801984150.005350.002125.00VolvoV40D3 R Design6
92015503000110000DieselIndividualManualFirst Owner14.102179147.947320.002250.00TataSafariStorme EX4
14201664500011000PetrolDealerAutomaticFirst Owner14.301598103.505153.003800.00SkodaRapid1 6 MPI AT Elegance4
2820196500005621PetrolTrustmark DealerAutomaticFirst Owner22.00119781.805113.004200.00MarutiSwiftAMT VVT VXI4
32201955000008500DieselDealerAutomaticFirst Owner16.781995190.005400.002125.00BMWX4M Sport X xDrive20d4
8201375000079328DieselTrustmark DealerManualSecond Owner12.992494100.607200.002400.00ToyotaInnova2 5 VX 7 Seater3
18201745000056290DieselDealerManualFirst Owner24.00118673.975190.242000.00HyundaiGrandi10 1 2 CRDi Sportz3